Taphonomic model of decomposition.

Area of Science:

• Forensic Taphonomy
• Forensic Science
• Decomposition Studies

Background:

• Human decomposition involves autolysis and putrefaction, complicating forensic analysis, especially post-mortem interval (PMI) estimation.
• Decomposition rates are influenced by intrinsic and extrinsic factors, varying geographically, necessitating regional models for accurate PMI determination.

Purpose of the Study:

• To develop a regional taphonomic model for decomposition in Barcelona, Catalonia, Spain.
• To analyze the influence of intrinsic and extrinsic factors on decomposition patterns and rates in this specific geographical area.
• To create predictive machine learning algorithms for probabilistic PMI estimation.

Main Methods:

• Statistical modeling to identify significant factors influencing decomposition.
• Analysis of intrinsic factors (cause, manner of death, pathology) and extrinsic factors (temperature).
• Development and validation of two machine learning algorithms for PMI estimation.

Main Results:

• Temperature and post-mortem interval (PMI) were identified as the most significant factors affecting decomposition.
• Other intrinsic factors like cause of death and pathological conditions also play a crucial role.
• Machine learning algorithms achieved reliable classification for PMI estimation in defined intervals (e.g., 1-2 days, 3-10 days, >10 days).

Conclusions:

• Machine learning offers a promising approach to enhance objectivity in forensic PMI assessments.
• The developed taphonomic model and algorithms can aid future research in forensic taphonomy.
• Regional decomposition models are essential for improving the precision of PMI estimation.